Data Directories:

EDA:

MovieLens Data:

IMDb Data:

IMDb movies:

Null values:

Depending on the visualization in question, we will need to decide what to do with the null values.

IMDb names:

Null values:

Null values:

IMDb Ratings:

Null values:

IMDb Principals:

Null values:

Merging it together:

Filtering the data needed:

Working on the IMDb data only:

Remove the row where the value for column 'year' is 'TV Movie 2019'

Separate the countries so we can analyze them separately. This piece of code will be used later on (in the section "top countries creating movies").

Separate the genre so we can analyze them separately This piece of code will be used later on.

Distribution per year:

Get the growth year to year (in %):

Get the top rated movies:

Top 10 rated movie with the year and the country

All time best movies:

Let's consider the best movies of this century:

Let's see which year was the best year for movies production:

Top countries creating movies:

Let's see how each country conributes in the volume of all movies produced:

Genre:

Exploring Plotly:

Producction Companies:

If we choose only this century: